{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://github.com/facebookresearch/dinov2/zipball/main\" to /data/home/xuehuiyu/.cache/torch/hub/main.zip\n",
      "/data/home/xuehuiyu/.cache/torch/hub/facebookresearch_dinov2_main/dinov2/layers/swiglu_ffn.py:43: UserWarning: xFormers is available (SwiGLU)\n",
      "  warnings.warn(\"xFormers is available (SwiGLU)\")\n",
      "/data/home/xuehuiyu/.cache/torch/hub/facebookresearch_dinov2_main/dinov2/layers/attention.py:27: UserWarning: xFormers is available (Attention)\n",
      "  warnings.warn(\"xFormers is available (Attention)\")\n",
      "/data/home/xuehuiyu/.cache/torch/hub/facebookresearch_dinov2_main/dinov2/layers/block.py:33: UserWarning: xFormers is available (Block)\n",
      "  warnings.warn(\"xFormers is available (Block)\")\n",
      "Downloading: \"https://dl.fbaipublicfiles.com/dinov2/dinov2_vitl14/dinov2_vitl14_reg4_pretrain.pth\" to /data/home/xuehuiyu/.cache/torch/hub/checkpoints/dinov2_vitl14_reg4_pretrain.pth\n",
      "100%|██████████| 1.13G/1.13G [02:05<00:00, 9.73MB/s] \n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "# => /data/home/xuehuiyu/.cache/torch/hub/checkpoints/dinov2_vitl14_reg4_pretrain.pth\n",
    "dinov2_vitl14_reg = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitl14_reg')\n",
    "# dinov2_vitg14_reg = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitg14_reg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mSignature:\u001b[0m        \u001b[0mdinov2_vitl14_reg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mType:\u001b[0m            DinoVisionTransformer\n",
      "\u001b[0;31mString form:\u001b[0m    \n",
      "DinoVisionTransformer(\n",
      "  (patch_embed): PatchEmbed(\n",
      "    (proj): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14))\n",
      "    (norm): Identity()\n",
      "  )\n",
      "  (blocks): ModuleList(\n",
      "    (0-23): 24 x NestedTensorBlock(\n",
      "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
      "      (attn): MemEffAttention(\n",
      "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
      "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
      "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "      )\n",
      "      (ls1): LayerScale()\n",
      "      (drop_path1): Identity()\n",
      "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
      "      (mlp): Mlp(\n",
      "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
      "        (act): GELU(approximate='none')\n",
      "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
      "        (drop): Dropout(p=0.0, inplace=False)\n",
      "      )\n",
      "      (ls2): LayerScale()\n",
      "      (drop_path2): Identity()\n",
      "    )\n",
      "  )\n",
      "  (norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
      "  (head): Identity()\n",
      ")\n",
      "\u001b[0;31mFile:\u001b[0m            ~/.cache/torch/hub/facebookresearch_dinov2_main/dinov2/models/vision_transformer.py\n",
      "\u001b[0;31mSource:\u001b[0m         \n",
      "\u001b[0;32mclass\u001b[0m \u001b[0mDinoVisionTransformer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModule\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mimg_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m224\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mpatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0min_chans\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0membed_dim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m768\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mdepth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mnum_heads\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mmlp_ratio\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mqkv_bias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mffn_bias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mproj_bias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mdrop_path_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mdrop_path_uniform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0minit_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;31m# for layerscale: None or 0 => no layerscale\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0membed_layer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mPatchEmbed\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mact_layer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGELU\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mblock_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mBlock\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mffn_layer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"mlp\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mblock_chunks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mnum_register_tokens\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0minterpolate_antialias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0minterpolate_offset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"\u001b[0m\n",
      "\u001b[0;34m        Args:\u001b[0m\n",
      "\u001b[0;34m            img_size (int, tuple): input image size\u001b[0m\n",
      "\u001b[0;34m            patch_size (int, tuple): patch size\u001b[0m\n",
      "\u001b[0;34m            in_chans (int): number of input channels\u001b[0m\n",
      "\u001b[0;34m            embed_dim (int): embedding dimension\u001b[0m\n",
      "\u001b[0;34m            depth (int): depth of transformer\u001b[0m\n",
      "\u001b[0;34m            num_heads (int): number of attention heads\u001b[0m\n",
      "\u001b[0;34m            mlp_ratio (int): ratio of mlp hidden dim to embedding dim\u001b[0m\n",
      "\u001b[0;34m            qkv_bias (bool): enable bias for qkv if True\u001b[0m\n",
      "\u001b[0;34m            proj_bias (bool): enable bias for proj in attn if True\u001b[0m\n",
      "\u001b[0;34m            ffn_bias (bool): enable bias for ffn if True\u001b[0m\n",
      "\u001b[0;34m            drop_path_rate (float): stochastic depth rate\u001b[0m\n",
      "\u001b[0;34m            drop_path_uniform (bool): apply uniform drop rate across blocks\u001b[0m\n",
      "\u001b[0;34m            weight_init (str): weight init scheme\u001b[0m\n",
      "\u001b[0;34m            init_values (float): layer-scale init values\u001b[0m\n",
      "\u001b[0;34m            embed_layer (nn.Module): patch embedding layer\u001b[0m\n",
      "\u001b[0;34m            act_layer (nn.Module): MLP activation layer\u001b[0m\n",
      "\u001b[0;34m            block_fn (nn.Module): transformer block class\u001b[0m\n",
      "\u001b[0;34m            ffn_layer (str): \"mlp\", \"swiglu\", \"swiglufused\" or \"identity\"\u001b[0m\n",
      "\u001b[0;34m            block_chunks: (int) split block sequence into block_chunks units for FSDP wrap\u001b[0m\n",
      "\u001b[0;34m            num_register_tokens: (int) number of extra cls tokens (so-called \"registers\")\u001b[0m\n",
      "\u001b[0;34m            interpolate_antialias: (str) flag to apply anti-aliasing when interpolating positional embeddings\u001b[0m\n",
      "\u001b[0;34m            interpolate_offset: (float) work-around offset to apply when interpolating positional embeddings\u001b[0m\n",
      "\u001b[0;34m        \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mnorm_layer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpartial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLayerNorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_features\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membed_dim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0membed_dim\u001b[0m  \u001b[0;31m# num_features for consistency with other models\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_tokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdepth\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_heads\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_heads\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpatch_size\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_register_tokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_register_tokens\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterpolate_antialias\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minterpolate_antialias\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterpolate_offset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minterpolate_offset\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch_embed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0membed_layer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimg_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0min_chans\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0min_chans\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membed_dim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0membed_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mnum_patches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch_embed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_patches\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcls_token\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mParameter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membed_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpos_embed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mParameter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_patches\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_tokens\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membed_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32massert\u001b[0m \u001b[0mnum_register_tokens\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister_tokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mParameter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_register_tokens\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membed_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnum_register_tokens\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mdrop_path_uniform\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mdpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdrop_path_rate\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdepth\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mdpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop_path_rate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdepth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m# stochastic depth decay rule\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mffn_layer\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"mlp\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"using MLP layer as FFN\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mffn_layer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMlp\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32melif\u001b[0m \u001b[0mffn_layer\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"swiglufused\"\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mffn_layer\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"swiglu\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"using SwiGLU layer as FFN\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mffn_layer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSwiGLUFFNFused\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32melif\u001b[0m \u001b[0mffn_layer\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"identity\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"using Identity layer as FFN\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0;32mreturn\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIdentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mffn_layer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mblocks_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mblock_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0membed_dim\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mnum_heads\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_heads\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mmlp_ratio\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmlp_ratio\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mqkv_bias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mqkv_bias\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mproj_bias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mproj_bias\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mffn_bias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mffn_bias\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mdrop_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mnorm_layer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnorm_layer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mact_layer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mact_layer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mffn_layer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mffn_layer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0minit_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minit_values\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdepth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mblock_chunks\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchunked_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mchunked_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mchunksize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdepth\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mblock_chunks\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdepth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunksize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0;31m# this is to keep the block index consistent if we chunk the block list\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mchunked_blocks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIdentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mblocks_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mchunksize\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModuleList\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mBlockChunk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mchunked_blocks\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchunked_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModuleList\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblocks_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnorm_layer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membed_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIdentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmask_token\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mParameter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membed_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0minit_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mtrunc_normal_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpos_embed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.02\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormal_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcls_token\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister_tokens\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormal_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister_tokens\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mnamed_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minit_weights_vit_timm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0minterpolate_pos_encoding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mprevious_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mnpatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mN\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpos_embed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mnpatch\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mN\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mw\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpos_embed\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mpos_embed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpos_embed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mclass_pos_embed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpos_embed\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mpatch_pos_embed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpos_embed\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mdim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mw0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mw\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch_size\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mh0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch_size\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mM\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Recover the number of patches in each dimension\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32massert\u001b[0m \u001b[0mN\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mM\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterpolate_offset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;31m# Historical kludge: add a small number to avoid floating point error in the interpolation, see https://github.com/facebookresearch/dino/issues/8\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;31m# Note: still needed for backward-compatibility, the underlying operators are using both output size and scale factors\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0msx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw0\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterpolate_offset\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0msy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh0\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterpolate_offset\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"scale_factor\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;31m# Simply specify an output size instead of a scale factor\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"size\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mw0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mpatch_pos_embed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunctional\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mpatch_pos_embed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpermute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"bicubic\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mantialias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterpolate_antialias\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32massert\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mw0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mpatch_pos_embed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mpatch_pos_embed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpatch_pos_embed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpermute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclass_pos_embed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpatch_pos_embed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprevious_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0mprepare_tokens_with_masks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmasks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mB\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch_embed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mmasks\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmasks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmask_token\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcls_token\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterpolate_pos_encoding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister_tokens\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                    \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                    \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister_tokens\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                    \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0mforward_features_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmasks_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprepare_tokens_with_masks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmasks\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmasks\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmasks_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mfor\u001b[0m \u001b[0mblk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mblk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mall_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmasks\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmasks_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mx_norm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0;34m{\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                    \u001b[0;34m\"x_norm_clstoken\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx_norm\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                    \u001b[0;34m\"x_norm_regtokens\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx_norm\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_register_tokens\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                    \u001b[0;34m\"x_norm_patchtokens\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx_norm\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_register_tokens\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                    \u001b[0;34m\"x_prenorm\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                    \u001b[0;34m\"masks\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmasks\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0mforward_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmasks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_features_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprepare_tokens_with_masks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mfor\u001b[0m \u001b[0mblk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mblk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mx_norm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;34m\"x_norm_clstoken\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx_norm\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;34m\"x_norm_regtokens\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx_norm\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_register_tokens\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;34m\"x_norm_patchtokens\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx_norm\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_register_tokens\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;34m\"x_prenorm\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;34m\"masks\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmasks\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0m_get_intermediate_layers_not_chunked\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprepare_tokens_with_masks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;31m# If n is an int, take the n last blocks. If it's a list, take them\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_block_len\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mblocks_to_take\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtotal_block_len\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_block_len\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mblk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mblocks_to_take\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblocks_to_take\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf\"only {len(output)} / {len(blocks_to_take)} blocks found\"\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0m_get_intermediate_layers_chunked\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprepare_tokens_with_masks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_block_len\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;31m# If n is an int, take the n last blocks. If it's a list, take them\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mblocks_to_take\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtotal_block_len\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_block_len\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mfor\u001b[0m \u001b[0mblock_chunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;32mfor\u001b[0m \u001b[0mblk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mblock_chunk\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Passing the nn.Identity()\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mblk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mblocks_to_take\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                    \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mi\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblocks_to_take\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf\"only {len(output)} / {len(blocks_to_take)} blocks found\"\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0mget_intermediate_layers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mn\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;31m# Layers or n last layers to take\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mreshape\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mreturn_class_token\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mnorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchunked_blocks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_intermediate_layers_chunked\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_intermediate_layers_not_chunked\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mout\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mclass_tokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mout\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_register_tokens\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mout\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mreshape\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mB\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mB\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpermute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontiguous\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0;32mfor\u001b[0m \u001b[0mout\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mreturn_class_token\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;32mreturn\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_tokens\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_training\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mis_training\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"x_norm_clstoken\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mClass docstring:\u001b[0m\n",
      "Base class for all neural network modules.\n",
      "\n",
      "Your models should also subclass this class.\n",
      "\n",
      "Modules can also contain other Modules, allowing to nest them in\n",
      "a tree structure. You can assign the submodules as regular attributes::\n",
      "\n",
      "    import torch.nn as nn\n",
      "    import torch.nn.functional as F\n",
      "\n",
      "    class Model(nn.Module):\n",
      "        def __init__(self):\n",
      "            super().__init__()\n",
      "            self.conv1 = nn.Conv2d(1, 20, 5)\n",
      "            self.conv2 = nn.Conv2d(20, 20, 5)\n",
      "\n",
      "        def forward(self, x):\n",
      "            x = F.relu(self.conv1(x))\n",
      "            return F.relu(self.conv2(x))\n",
      "\n",
      "Submodules assigned in this way will be registered, and will have their\n",
      "parameters converted too when you call :meth:`to`, etc.\n",
      "\n",
      ".. note::\n",
      "    As per the example above, an ``__init__()`` call to the parent class\n",
      "    must be made before assignment on the child.\n",
      "\n",
      ":ivar training: Boolean represents whether this module is in training or\n",
      "                evaluation mode.\n",
      ":vartype training: bool\n",
      "\u001b[0;31mInit docstring:\u001b[0m \n",
      "Args:\n",
      "    img_size (int, tuple): input image size\n",
      "    patch_size (int, tuple): patch size\n",
      "    in_chans (int): number of input channels\n",
      "    embed_dim (int): embedding dimension\n",
      "    depth (int): depth of transformer\n",
      "    num_heads (int): number of attention heads\n",
      "    mlp_ratio (int): ratio of mlp hidden dim to embedding dim\n",
      "    qkv_bias (bool): enable bias for qkv if True\n",
      "    proj_bias (bool): enable bias for proj in attn if True\n",
      "    ffn_bias (bool): enable bias for ffn if True\n",
      "    drop_path_rate (float): stochastic depth rate\n",
      "    drop_path_uniform (bool): apply uniform drop rate across blocks\n",
      "    weight_init (str): weight init scheme\n",
      "    init_values (float): layer-scale init values\n",
      "    embed_layer (nn.Module): patch embedding layer\n",
      "    act_layer (nn.Module): MLP activation layer\n",
      "    block_fn (nn.Module): transformer block class\n",
      "    ffn_layer (str): \"mlp\", \"swiglu\", \"swiglufused\" or \"identity\"\n",
      "    block_chunks: (int) split block sequence into block_chunks units for FSDP wrap\n",
      "    num_register_tokens: (int) number of extra cls tokens (so-called \"registers\")\n",
      "    interpolate_antialias: (str) flag to apply anti-aliasing when interpolating positional embeddings\n",
      "    interpolate_offset: (float) work-around offset to apply when interpolating positional embeddings"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in /data/home/xuehuiyu/.cache/torch/hub/facebookresearch_dinov2_main\n",
      "/data/home/xuehuiyu/.cache/torch/hub/facebookresearch_dinov2_main/dinov2/layers/swiglu_ffn.py:43: UserWarning: xFormers is available (SwiGLU)\n",
      "  warnings.warn(\"xFormers is available (SwiGLU)\")\n",
      "/data/home/xuehuiyu/.cache/torch/hub/facebookresearch_dinov2_main/dinov2/layers/attention.py:27: UserWarning: xFormers is available (Attention)\n",
      "  warnings.warn(\"xFormers is available (Attention)\")\n",
      "/data/home/xuehuiyu/.cache/torch/hub/facebookresearch_dinov2_main/dinov2/layers/block.py:33: UserWarning: xFormers is available (Block)\n",
      "  warnings.warn(\"xFormers is available (Block)\")\n",
      "Downloading: \"https://dl.fbaipublicfiles.com/dinov2/dinov2_vitg14/dinov2_vitg14_pretrain.pth\" to /data/home/xuehuiyu/.cache/torch/hub/checkpoints/dinov2_vitg14_pretrain.pth\n",
      "100%|██████████| 4.23G/4.23G [07:37<00:00, 9.93MB/s] \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DinoVisionTransformer(\n",
       "  (patch_embed): PatchEmbed(\n",
       "    (proj): Conv2d(3, 1536, kernel_size=(14, 14), stride=(14, 14))\n",
       "    (norm): Identity()\n",
       "  )\n",
       "  (blocks): ModuleList(\n",
       "    (0-39): 40 x NestedTensorBlock(\n",
       "      (norm1): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n",
       "      (attn): MemEffAttention(\n",
       "        (qkv): Linear(in_features=1536, out_features=4608, bias=True)\n",
       "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "        (proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
       "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "      (ls1): LayerScale()\n",
       "      (drop_path1): Identity()\n",
       "      (norm2): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n",
       "      (mlp): SwiGLUFFNFused(\n",
       "        (w12): Linear(in_features=1536, out_features=8192, bias=True)\n",
       "        (w3): Linear(in_features=4096, out_features=1536, bias=True)\n",
       "      )\n",
       "      (ls2): LayerScale()\n",
       "      (drop_path2): Identity()\n",
       "    )\n",
       "  )\n",
       "  (norm): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n",
       "  (head): Identity()\n",
       ")"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "BACKBONE_SIZE = \"giant\" # in (\"small\", \"base\", \"large\" or \"giant\")\n",
    "\n",
    "\n",
    "backbone_archs = {\n",
    "    \"small\": \"vits14\",\n",
    "    \"base\": \"vitb14\",\n",
    "    \"large\": \"vitl14\",\n",
    "    \"giant\": \"vitg14\",\n",
    "}\n",
    "backbone_arch = backbone_archs[BACKBONE_SIZE]\n",
    "backbone_name = f\"dinov2_{backbone_arch}\"\n",
    "\n",
    "backbone_model = torch.hub.load(repo_or_dir=\"facebookresearch/dinov2\", model=backbone_name)\n",
    "backbone_model.eval()\n",
    "backbone_model.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=640x480>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import urllib\n",
    "\n",
    "from PIL import Image\n",
    "\n",
    "\n",
    "def load_image_from_url(url: str) -> Image:\n",
    "    with urllib.request.urlopen(url) as f:\n",
    "        return Image.open(f).convert(\"RGB\")\n",
    "\n",
    "\n",
    "EXAMPLE_IMAGE_URL = \"https://dl.fbaipublicfiles.com/dinov2/images/example.jpg\"\n",
    "\n",
    "\n",
    "image = load_image_from_url(EXAMPLE_IMAGE_URL)\n",
    "display(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mmseg.apis import init_segmentor, inference_segmentor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mSignature:\u001b[0m  \u001b[0minference_segmentor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mSource:\u001b[0m   \n",
      "\u001b[0;32mdef\u001b[0m \u001b[0minference_segmentor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;34m\"\"\"Inference image(s) with the segmentor.\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m    Args:\u001b[0m\n",
      "\u001b[0;34m        model (nn.Module): The loaded segmentor.\u001b[0m\n",
      "\u001b[0;34m        imgs (str/ndarray or list[str/ndarray]): Either image files or loaded\u001b[0m\n",
      "\u001b[0;34m            images.\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m    Returns:\u001b[0m\n",
      "\u001b[0;34m        (list[Tensor]): The segmentation result.\u001b[0m\n",
      "\u001b[0;34m    \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mcfg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m  \u001b[0;31m# model device\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;31m# build the data pipeline\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mtest_pipeline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mLoadImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpipeline\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mtest_pipeline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCompose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_pipeline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;31m# prepare data\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_pipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcollate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msamples_per_gpu\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mif\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_cuda\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;31m# scatter to specified GPU\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'img_metas'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'img_metas'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;31m# forward the model\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreturn_loss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrescale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFile:\u001b[0m      ~/miniconda3/envs/pt200cu117/lib/python3.9/site-packages/mmseg/apis/inference.py\n",
      "\u001b[0;31mType:\u001b[0m      function"
     ]
    }
   ],
   "source": [
    "?? inference_segmentor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import itertools\n",
    "from functools import partial\n",
    "\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from mmseg.apis import init_segmentor, inference_segmentor\n",
    "\n",
    "import dinov2.eval.segmentation.models\n",
    "\n",
    "\n",
    "class CenterPadding(torch.nn.Module):\n",
    "    def __init__(self, multiple):\n",
    "        super().__init__()\n",
    "        self.multiple = multiple\n",
    "\n",
    "    def _get_pad(self, size):\n",
    "        new_size = math.ceil(size / self.multiple) * self.multiple\n",
    "        pad_size = new_size - size\n",
    "        pad_size_left = pad_size // 2\n",
    "        pad_size_right = pad_size - pad_size_left\n",
    "        return pad_size_left, pad_size_right\n",
    "\n",
    "    @torch.inference_mode()\n",
    "    def forward(self, x):\n",
    "        pads = list(itertools.chain.from_iterable(self._get_pad(m) for m in x.shape[:1:-1]))\n",
    "        output = F.pad(x, pads)\n",
    "        return output\n",
    "\n",
    "\n",
    "def create_segmenter(cfg, backbone_model):\n",
    "    model = init_segmentor(cfg)\n",
    "    model.backbone.forward = partial(\n",
    "        backbone_model.get_intermediate_layers,\n",
    "        n=cfg.model.backbone.out_indices,\n",
    "        reshape=True,\n",
    "    )\n",
    "    if hasattr(backbone_model, \"patch_size\"):\n",
    "        model.backbone.register_forward_pre_hook(lambda _, x: CenterPadding(backbone_model.patch_size)(x[0]))\n",
    "    model.init_weights()\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scales: [1.0, 1.32, 1.73]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/home/xuehuiyu/miniconda3/envs/pt200cu117/lib/python3.9/site-packages/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``.\n",
      "  warnings.warn(\n",
      "2024-04-22 19:39:25,428 - mmcv - INFO - initialize BNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}}\n",
      "2024-04-22 19:39:25,452 - mmcv - INFO - \n",
      "decode_head.conv_seg.weight - torch.Size([21, 6144, 1, 1]): \n",
      "NormalInit: mean=0, std=0.01, bias=0 \n",
      " \n",
      "2024-04-22 19:39:25,453 - mmcv - INFO - \n",
      "decode_head.conv_seg.bias - torch.Size([21]): \n",
      "NormalInit: mean=0, std=0.01, bias=0 \n",
      " \n",
      "2024-04-22 19:39:25,453 - mmcv - INFO - \n",
      "decode_head.bn.weight - torch.Size([6144]): \n",
      "The value is the same before and after calling `init_weights` of EncoderDecoder  \n",
      " \n",
      "2024-04-22 19:39:25,453 - mmcv - INFO - \n",
      "decode_head.bn.bias - torch.Size([6144]): \n",
      "The value is the same before and after calling `init_weights` of EncoderDecoder  \n",
      " \n",
      "Downloading: \"https://dl.fbaipublicfiles.com/dinov2/dinov2_vitg14/dinov2_vitg14_voc2012_ms_head.pth\" to /data/home/xuehuiyu/.cache/torch/hub/checkpoints/dinov2_vitg14_voc2012_ms_head.pth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load checkpoint from http path: https://dl.fbaipublicfiles.com/dinov2/dinov2_vitg14/dinov2_vitg14_voc2012_ms_head.pth\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1.67M/1.67M [00:01<00:00, 1.22MB/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "EncoderDecoder(\n",
       "  (backbone): DinoVisionTransformer()\n",
       "  (decode_head): BNHead(\n",
       "    input_transform=resize_concat, ignore_index=255, align_corners=False\n",
       "    (loss_decode): CrossEntropyLoss(avg_non_ignore=False)\n",
       "    (conv_seg): Conv2d(6144, 21, kernel_size=(1, 1), stride=(1, 1))\n",
       "    (bn): SyncBatchNorm(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}}\n",
       ")"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import urllib\n",
    "\n",
    "import mmcv\n",
    "from mmcv.runner import load_checkpoint\n",
    "\n",
    "\n",
    "def load_config_from_url(url: str) -> str:\n",
    "    with urllib.request.urlopen(url) as f:\n",
    "        return f.read().decode()\n",
    "\n",
    "\n",
    "HEAD_SCALE_COUNT = 3 # more scales: slower but better results, in (1,2,3,4,5)\n",
    "HEAD_DATASET = \"voc2012\" # in (\"ade20k\", \"voc2012\")\n",
    "HEAD_TYPE = \"ms\" # in (\"ms, \"linear\")\n",
    "\n",
    "\n",
    "DINOV2_BASE_URL = \"https://dl.fbaipublicfiles.com/dinov2\"\n",
    "head_config_url = f\"{DINOV2_BASE_URL}/{backbone_name}/{backbone_name}_{HEAD_DATASET}_{HEAD_TYPE}_config.py\"\n",
    "head_checkpoint_url = f\"{DINOV2_BASE_URL}/{backbone_name}/{backbone_name}_{HEAD_DATASET}_{HEAD_TYPE}_head.pth\"\n",
    "\n",
    "cfg_str = load_config_from_url(head_config_url)\n",
    "cfg = mmcv.Config.fromstring(cfg_str, file_format=\".py\")\n",
    "if HEAD_TYPE == \"ms\":\n",
    "    cfg.data.test.pipeline[1][\"img_ratios\"] = cfg.data.test.pipeline[1][\"img_ratios\"][:HEAD_SCALE_COUNT]\n",
    "    print(\"scales:\", cfg.data.test.pipeline[1][\"img_ratios\"])\n",
    "\n",
    "model = create_segmenter(cfg, backbone_model=backbone_model)\n",
    "load_checkpoint(model, head_checkpoint_url, map_location=\"cpu\")\n",
    "model.cuda()\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/home/xuehuiyu/miniconda3/envs/pt200cu117/lib/python3.9/site-packages/xformers/ops/unbind.py:46: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  storage_data_ptr = tensors[0].storage().data_ptr()\n",
      "/data/home/xuehuiyu/miniconda3/envs/pt200cu117/lib/python3.9/site-packages/xformers/ops/unbind.py:48: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  if x.storage().data_ptr() != storage_data_ptr:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=640x480>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "import dinov2.eval.segmentation.utils.colormaps as colormaps\n",
    "\n",
    "\n",
    "DATASET_COLORMAPS = {\n",
    "    \"ade20k\": colormaps.ADE20K_COLORMAP,\n",
    "    \"voc2012\": colormaps.VOC2012_COLORMAP,\n",
    "}\n",
    "\n",
    "\n",
    "def render_segmentation(segmentation_logits, dataset):\n",
    "    colormap = DATASET_COLORMAPS[dataset]\n",
    "    colormap_array = np.array(colormap, dtype=np.uint8)\n",
    "    segmentation_values = colormap_array[segmentation_logits + 1]\n",
    "    return Image.fromarray(segmentation_values)\n",
    "\n",
    "\n",
    "array = np.array(image)[:, :, ::-1] # BGR\n",
    "segmentation_logits = inference_segmentor(model, array)[0]\n",
    "segmented_image = render_segmentation(segmentation_logits, HEAD_DATASET)\n",
    "display(segmented_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mmseg.apis import *\n",
    "\n",
    "\n",
    "def inference_feat(model, img):\n",
    "    \"\"\"Inference image(s) with the segmentor.\n",
    "\n",
    "    Args:\n",
    "        model (nn.Module): The loaded segmentor.\n",
    "        imgs (str/ndarray or list[str/ndarray]): Either image files or loaded\n",
    "            images.\n",
    "\n",
    "    Returns:\n",
    "        (list[Tensor]): The segmentation result.\n",
    "    \"\"\"\n",
    "    cfg = model.cfg\n",
    "    device = next(model.parameters()).device  # model device\n",
    "    # build the data pipeline\n",
    "    test_pipeline = [LoadImage()] + cfg.data.test.pipeline[1:]\n",
    "    test_pipeline = Compose(test_pipeline)\n",
    "    # prepare data\n",
    "    data = dict(img=img)\n",
    "    data = test_pipeline(data)\n",
    "    data = collate([data], samples_per_gpu=1)\n",
    "    if next(model.parameters()).is_cuda:\n",
    "        # scatter to specified GPU\n",
    "        data = scatter(data, [device])[0]\n",
    "    else:\n",
    "        data['img_metas'] = [i.data[0] for i in data['img_metas']]\n",
    "\n",
    "    # forward the model\n",
    "    with torch.no_grad():\n",
    "        result = model(return_loss=False, rescale=True, **data)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'slide'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mSignature:\u001b[0m       \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mType:\u001b[0m           EncoderDecoder\n",
      "\u001b[0;31mString form:\u001b[0m   \n",
      "EncoderDecoder(\n",
      "  (backbone): DinoVisionTransformer()\n",
      "  (decode_head): BNHead(\n",
      "    input_transform=resize_concat, ignore_index=255, align_corners=False\n",
      "    (loss_decode): CrossEntropyLoss(avg_non_ignore=False)\n",
      "    (conv_seg): Conv2d(6144, 21, kernel_size=(1, 1), stride=(1, 1))\n",
      "    (bn): SyncBatchNorm(6144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}}\n",
      ")\n",
      "\u001b[0;31mFile:\u001b[0m           ~/miniconda3/envs/pt200cu117/lib/python3.9/site-packages/mmseg/models/segmentors/encoder_decoder.py\n",
      "\u001b[0;31mSource:\u001b[0m        \n",
      "\u001b[0;34m@\u001b[0m\u001b[0mSEGMENTORS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;32mclass\u001b[0m \u001b[0mEncoderDecoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBaseSegmentor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;34m\"\"\"Encoder Decoder segmentors.\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m    EncoderDecoder typically consists of backbone, decode_head, auxiliary_head.\u001b[0m\n",
      "\u001b[0;34m    Note that auxiliary_head is only used for deep supervision during training,\u001b[0m\n",
      "\u001b[0;34m    which could be dumped during inference.\u001b[0m\n",
      "\u001b[0;34m    \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                 \u001b[0mbackbone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                 \u001b[0mdecode_head\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                 \u001b[0mneck\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                 \u001b[0mauxiliary_head\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                 \u001b[0mtrain_cfg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                 \u001b[0mtest_cfg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                 \u001b[0mpretrained\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                 \u001b[0minit_cfg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEncoderDecoder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minit_cfg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mpretrained\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;32massert\u001b[0m \u001b[0mbackbone\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'pretrained'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \\\n",
      "                \u001b[0;34m'both backbone and segmentor set pretrained weight'\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mbackbone\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpretrained\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpretrained\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackbone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_backbone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbackbone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mneck\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mneck\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_neck\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mneck\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_decode_head\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdecode_head\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_auxiliary_head\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mauxiliary_head\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_cfg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_cfg\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_cfg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_cfg\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_decode_head\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0m_init_decode_head\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecode_head\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"Initialize ``decode_head``\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode_head\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_head\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdecode_head\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malign_corners\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode_head\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malign_corners\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_classes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode_head\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_classes\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0m_init_auxiliary_head\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mauxiliary_head\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"Initialize ``auxiliary_head``\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mauxiliary_head\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mauxiliary_head\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauxiliary_head\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModuleList\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0;32mfor\u001b[0m \u001b[0mhead_cfg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mauxiliary_head\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                    \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauxiliary_head\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_head\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhead_cfg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauxiliary_head\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_head\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mauxiliary_head\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0mextract_feat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"Extract features from images.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackbone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_neck\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mneck\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0mencode_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_metas\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"Encode images with backbone and decode into a semantic segmentation\u001b[0m\n",
      "\u001b[0;34m        map of the same size as input.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextract_feat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decode_head_forward_test\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_metas\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0minput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bilinear'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0malign_corners\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malign_corners\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0m_decode_head_forward_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_metas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgt_semantic_seg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"Run forward function and calculate loss for decode head in\u001b[0m\n",
      "\u001b[0;34m        training.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mlosses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mloss_decode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode_head\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_metas\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                                                     \u001b[0mgt_semantic_seg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                                                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_cfg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mlosses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madd_prefix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss_decode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'decode'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mlosses\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0m_decode_head_forward_test\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_metas\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"Run forward function and calculate loss for decode head in\u001b[0m\n",
      "\u001b[0;34m        inference.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mseg_logits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode_head\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_test\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_metas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_cfg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mseg_logits\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0m_auxiliary_head_forward_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_metas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgt_semantic_seg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"Run forward function and calculate loss for auxiliary head in\u001b[0m\n",
      "\u001b[0;34m        training.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mlosses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauxiliary_head\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModuleList\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maux_head\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauxiliary_head\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mloss_aux\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maux_head\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_metas\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                                                  \u001b[0mgt_semantic_seg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                                                  \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_cfg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mlosses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madd_prefix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss_aux\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf'aux_{idx}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mloss_aux\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauxiliary_head\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_metas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgt_semantic_seg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_cfg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mlosses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madd_prefix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss_aux\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'aux'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mlosses\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0mforward_dummy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"Dummy forward function.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mseg_logit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mseg_logit\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0mforward_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_metas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgt_semantic_seg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"Forward function for training.\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m        Args:\u001b[0m\n",
      "\u001b[0;34m            img (Tensor): Input images.\u001b[0m\n",
      "\u001b[0;34m            img_metas (list[dict]): List of image info dict where each dict\u001b[0m\n",
      "\u001b[0;34m                has: 'img_shape', 'scale_factor', 'flip', and may also contain\u001b[0m\n",
      "\u001b[0;34m                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.\u001b[0m\n",
      "\u001b[0;34m                For details on the values of these keys see\u001b[0m\n",
      "\u001b[0;34m                `mmseg/datasets/pipelines/formatting.py:Collect`.\u001b[0m\n",
      "\u001b[0;34m            gt_semantic_seg (Tensor): Semantic segmentation masks\u001b[0m\n",
      "\u001b[0;34m                used if the architecture supports semantic segmentation task.\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m        Returns:\u001b[0m\n",
      "\u001b[0;34m            dict[str, Tensor]: a dictionary of loss components\u001b[0m\n",
      "\u001b[0;34m        \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextract_feat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mlosses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mloss_decode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decode_head_forward_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_metas\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                                                      \u001b[0mgt_semantic_seg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mlosses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss_decode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_auxiliary_head\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mloss_aux\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_auxiliary_head_forward_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_metas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgt_semantic_seg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mlosses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss_aux\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mlosses\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;31m# TODO refactor\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0mslide_inference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_meta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrescale\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"Inference by sliding-window with overlap.\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m        If h_crop > h_img or w_crop > w_img, the small patch will be used to\u001b[0m\n",
      "\u001b[0;34m        decode without padding.\u001b[0m\n",
      "\u001b[0;34m        \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mh_stride\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw_stride\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_cfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mh_crop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw_crop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_cfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcrop_size\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh_img\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mnum_classes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_classes\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mh_grids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh_img\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mh_crop\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mh_stride\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mh_stride\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mw_grids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw_img\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mw_crop\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mw_stride\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mw_stride\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_zeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_classes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh_img\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw_img\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mcount_mat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_zeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh_img\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw_img\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mfor\u001b[0m \u001b[0mh_idx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh_grids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;32mfor\u001b[0m \u001b[0mw_idx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw_grids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0my1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh_idx\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mh_stride\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mx1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mw_idx\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mw_stride\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0my2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mh_crop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh_img\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mx2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mw_crop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw_img\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0my1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my2\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mh_crop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mx1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx2\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mw_crop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mcrop_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0my2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mx2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mcrop_seg_logit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcrop_img\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_meta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mpreds\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcrop_seg_logit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                               \u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mx2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                                \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0my2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mcount_mat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0my2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mx2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32massert\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcount_mat\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0monnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_in_onnx_export\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;31m# cast count_mat to constant while exporting to ONNX\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mcount_mat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mcount_mat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpreds\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mcount_mat\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mrescale\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;31m# remove padding area\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mresize_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg_meta\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'img_shape'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpreds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0mresize_shape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0mresize_shape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mpreds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimg_meta\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ori_shape'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bilinear'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0malign_corners\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malign_corners\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mwarning\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mpreds\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0mwhole_inference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_meta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrescale\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"Inference with full image.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mseg_logit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_meta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mrescale\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;31m# support dynamic shape for onnx\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0monnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_in_onnx_export\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0;31m# remove padding area\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mresize_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg_meta\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'img_shape'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mseg_logit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mseg_logit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0mresize_shape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0mresize_shape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg_meta\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ori_shape'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mseg_logit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mseg_logit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bilinear'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0malign_corners\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malign_corners\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0mwarning\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mseg_logit\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0minference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_meta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrescale\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"Inference with slide/whole style.\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m        Args:\u001b[0m\n",
      "\u001b[0;34m            img (Tensor): The input image of shape (N, 3, H, W).\u001b[0m\n",
      "\u001b[0;34m            img_meta (dict): Image info dict where each dict has: 'img_shape',\u001b[0m\n",
      "\u001b[0;34m                'scale_factor', 'flip', and may also contain\u001b[0m\n",
      "\u001b[0;34m                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.\u001b[0m\n",
      "\u001b[0;34m                For details on the values of these keys see\u001b[0m\n",
      "\u001b[0;34m                `mmseg/datasets/pipelines/formatting.py:Collect`.\u001b[0m\n",
      "\u001b[0;34m            rescale (bool): Whether rescale back to original shape.\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m        Returns:\u001b[0m\n",
      "\u001b[0;34m            Tensor: The output segmentation map.\u001b[0m\n",
      "\u001b[0;34m        \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_cfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'slide'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'whole'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mori_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg_meta\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ori_shape'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32massert\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ori_shape'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mori_shape\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mimg_meta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_cfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'slide'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mseg_logit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslide_inference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_meta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrescale\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mseg_logit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhole_inference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_meta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrescale\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msoftmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseg_logit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mflip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg_meta\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'flip'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mflip\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mflip_direction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg_meta\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'flip_direction'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;32massert\u001b[0m \u001b[0mflip_direction\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'horizontal'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'vertical'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;32mif\u001b[0m \u001b[0mflip_direction\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'horizontal'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;32melif\u001b[0m \u001b[0mflip_direction\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'vertical'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m                \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0msimple_test\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_meta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrescale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"Simple test with single image.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mseg_logit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_meta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrescale\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mseg_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mseg_logit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0monnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_in_onnx_export\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;31m# our inference backend only support 4D output\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mseg_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mseg_pred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0;32mreturn\u001b[0m \u001b[0mseg_pred\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mseg_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mseg_pred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;31m# unravel batch dim\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mseg_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseg_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mseg_pred\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0maug_test\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimgs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_metas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrescale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"Test with augmentations.\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m        Only rescale=True is supported.\u001b[0m\n",
      "\u001b[0;34m        \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;31m# aug_test rescale all imgs back to ori_shape for now\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32massert\u001b[0m \u001b[0mrescale\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;31m# to save memory, we get augmented seg logit inplace\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mseg_logit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimgs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_metas\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrescale\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimgs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mcur_seg_logit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimgs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_metas\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrescale\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m            \u001b[0mseg_logit\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mcur_seg_logit\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mseg_logit\u001b[0m \u001b[0;34m/=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimgs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mseg_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mseg_logit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mseg_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mseg_pred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;31m# unravel batch dim\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0mseg_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseg_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mseg_pred\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInit docstring:\u001b[0m Initialize BaseModule, inherited from `torch.nn.Module`"
     ]
    }
   ],
   "source": [
    "?? model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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